{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX D.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 09:53:50
{txt}
{com}. 
. 
. 
. 
. **** APPENDIX D STATISTICAL ANALYSES: 'LOYAL LIEUTENTANT' ADDITIVE/UNCONDITIONAL EFFECT OF PRESIDENTIAL LOYALTY ON APPOINTEE TENURE [SANS INTERACTION TERMS WITH PRESIDENTIAL POLICY PRIORITY BINARY INDICATOR] ***
. 
. 
. 
. 
. ** RETRIEVE SINGLE EVENT RECORDS DATABASE [N = 860 APPOINTEE OBSERVATIONS: 831 UNCENSORED OBSERVATIONS; 29 CENSORED OBSERVATIONS] **
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. 
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. *
. *
. *
. *
. *
. *
. *
. *
. 
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** APPENDIX D SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS ****
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** APPENDIX D REGRESSION MODELS: 'LOYAL LIEUTENTANT' ADDITIVE/UNCONDITIONAL EFFECT OF PRESIDENTIAL LOYALTY ON APPOINTEE TENURE [SANS INTERACTION TERMS WITH PRESIDENTIAL POLICY PRIORITY BINARY INDICATOR]  ***
. 
. 
. 
. **** MODEL D1: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   zloyalmedian soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  ,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4544.7993
{txt}Iteration 2:   log pseudolikelihood = {res}-4526.6971
{txt}Iteration 3:   log pseudolikelihood = {res}-4526.4159
{txt}Iteration 4:   log pseudolikelihood = {res}-4526.4156
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4526.4156

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1191.19
{txt}Log pseudolikelihood =   {res}-4526.4156             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.029733{col 40}{space 2} .0698031{col 51}{space 1}    0.43{col 60}{space 3}0.666{col 68}{space 4} .9016207{col 81}{space 3}  1.17605
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.021184{col 40}{space 2} .0998182{col 51}{space 1}    0.21{col 60}{space 3}0.830{col 68}{space 4} .8431433{col 81}{space 3} 1.236822
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9586654{col 40}{space 2} .0681942{col 51}{space 1}   -0.59{col 60}{space 3}0.553{col 68}{space 4} .8339063{col 81}{space 3} 1.102089
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.015068{col 40}{space 2} .0598067{col 51}{space 1}    0.25{col 60}{space 3}0.800{col 68}{space 4}  .904364{col 81}{space 3} 1.139323
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5745237{col 40}{space 2} .0485055{col 51}{space 1}   -6.56{col 60}{space 3}0.000{col 68}{space 4} .4869039{col 81}{space 3} .6779109
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.398812{col 40}{space 2} .1321944{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.162296{col 81}{space 3} 1.683456
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.337382{col 40}{space 2} .1282837{col 51}{space 1}    3.03{col 60}{space 3}0.002{col 68}{space 4} 1.108172{col 81}{space 3} 1.614002
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2}  1.02498{col 40}{space 2} .1632653{col 51}{space 1}    0.15{col 60}{space 3}0.877{col 68}{space 4} .7501194{col 81}{space 3} 1.400555
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .8414547{col 40}{space 2} .0865724{col 51}{space 1}   -1.68{col 60}{space 3}0.093{col 68}{space 4} .6877895{col 81}{space 3} 1.029452
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0014024{col 40}{space 2} .0035557{col 51}{space 1}   -2.59{col 60}{space 3}0.010{col 68}{space 4} 9.74e-06{col 81}{space 3} .2018358
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.594168{col 40}{space 2} .3587727{col 51}{space 1}    2.07{col 60}{space 3}0.038{col 68}{space 4} 1.025577{col 81}{space 3} 2.477991
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1992816{col 40}{space 2} .0353308{col 51}{space 1}   -9.10{col 60}{space 3}0.000{col 68}{space 4} .1407852{col 81}{space 3} .2820832
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9956977{col 40}{space 2} .0033281{col 51}{space 1}   -1.29{col 60}{space 3}0.197{col 68}{space 4} .9891959{col 81}{space 3} 1.002242
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9354113{col 40}{space 2} .0425703{col 51}{space 1}   -1.47{col 60}{space 3}0.142{col 68}{space 4}  .855588{col 81}{space 3} 1.022682
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.864076{col 40}{space 2} .3525484{col 51}{space 1}    3.29{col 60}{space 3}0.001{col 68}{space 4} 1.286701{col 81}{space 3} 2.700532
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 5.273613{col 40}{space 2} .7365807{col 51}{space 1}   11.90{col 60}{space 3}0.000{col 68}{space 4} 4.010684{col 81}{space 3} 6.934226
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.377493{col 40}{space 2} 1.343035{col 51}{space 1}    4.81{col 60}{space 3}0.000{col 68}{space 4} 2.399236{col 81}{space 3} 7.986895
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  1.31544{col 40}{space 2} .1547281{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.044597{col 81}{space 3} 1.656507
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.867894{col 40}{space 2} .3751528{col 51}{space 1}    8.05{col 60}{space 3}0.000{col 68}{space 4} 2.219302{col 81}{space 3} 3.706038
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 5.014718{col 40}{space 2} 1.231924{col 51}{space 1}    6.56{col 60}{space 3}0.000{col 68}{space 4}  3.09841{col 81}{space 3} 8.116225
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 7.524428{col 40}{space 2} 2.266445{col 51}{space 1}    6.70{col 60}{space 3}0.000{col 68}{space 4} 4.169478{col 81}{space 3} 13.57892
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4526.416{col 50}    21{col 58} 9094.831{col 69} 9194.727
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelD1
{txt}
{com}. 
. 
. *** COMPUTE Figure D2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. 
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE D2] **
. 
. 
. 
. lincomest zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.040815{col 26}{space 2} .0963294{col 37}{space 1}    0.43{col 46}{space 3}0.666{col 54}{space 4} .8681466{col 67}{space 3} 1.247825
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelD1zloyal = r(table)
{txt}
{com}. mat list modelD1zloyal
{res}
{txt}modelD1zloyal[9,1]
              (1)
     b {res} 1.0408146
{txt}    se {res}  .0963294
{txt}     z {res} .43222985
{txt}pvalue {res} .66557437
{txt}    ll {res} .86814655
{txt}    ul {res} 1.2478251
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. *
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL D2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   zloyalmedian soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4509.4622
{txt}Iteration 2:   log pseudolikelihood = {res}-4482.9781
{txt}Iteration 3:   log pseudolikelihood = {res}-4482.6116
{txt}Iteration 4:   log pseudolikelihood = {res}-4482.6112
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4482.6112

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}40{txt})    =  {res}  56460.64
{txt}Log pseudolikelihood =   {res}-4482.6112             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} .9952188{col 40}{space 2} .0745495{col 51}{space 1}   -0.06{col 60}{space 3}0.949{col 68}{space 4} .8593243{col 81}{space 3} 1.152604
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.048237{col 40}{space 2} .1743899{col 51}{space 1}    0.28{col 60}{space 3}0.777{col 68}{space 4} .7565705{col 81}{space 3} 1.452344
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.035432{col 40}{space 2} .0819319{col 51}{space 1}    0.44{col 60}{space 3}0.660{col 68}{space 4} .8866808{col 81}{space 3} 1.209137
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9755858{col 40}{space 2}  .064166{col 51}{space 1}   -0.38{col 60}{space 3}0.707{col 68}{space 4} .8575914{col 81}{space 3} 1.109815
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .4998114{col 40}{space 2} .0539237{col 51}{space 1}   -6.43{col 60}{space 3}0.000{col 68}{space 4} .4045494{col 81}{space 3} .6175054
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .6388171{col 40}{space 2} .1567147{col 51}{space 1}   -1.83{col 60}{space 3}0.068{col 68}{space 4}  .394966{col 81}{space 3} 1.033221
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2}  .619164{col 40}{space 2} .1569828{col 51}{space 1}   -1.89{col 60}{space 3}0.059{col 68}{space 4} .3766969{col 81}{space 3} 1.017699
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.471325{col 40}{space 2} .2370471{col 51}{space 1}    2.40{col 60}{space 3}0.017{col 68}{space 4} 1.072928{col 81}{space 3} 2.017655
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.912132{col 40}{space 2} .4846431{col 51}{space 1}    2.56{col 60}{space 3}0.011{col 68}{space 4} 1.163523{col 81}{space 3} 3.142395
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 5.96e-11{col 40}{space 2} 6.20e-10{col 51}{space 1}   -2.26{col 60}{space 3}0.024{col 68}{space 4} 8.31e-20{col 81}{space 3} .0427573
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 602.4461{col 40}{space 2} 1414.066{col 51}{space 1}    2.73{col 60}{space 3}0.006{col 68}{space 4} 6.053022{col 81}{space 3} 59960.35
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1697532{col 40}{space 2} .0382601{col 51}{space 1}   -7.87{col 60}{space 3}0.000{col 68}{space 4}  .109136{col 81}{space 3} .2640387
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9919724{col 40}{space 2} .0046387{col 51}{space 1}   -1.72{col 60}{space 3}0.085{col 68}{space 4} .9829223{col 81}{space 3} 1.001106
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2}  1.13089{col 40}{space 2} .1067765{col 51}{space 1}    1.30{col 60}{space 3}0.193{col 68}{space 4} .9398346{col 81}{space 3} 1.360784
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.649753{col 40}{space 2} .3719302{col 51}{space 1}    2.22{col 60}{space 3}0.026{col 68}{space 4}  1.06052{col 81}{space 3} 2.566368
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 3.910389{col 40}{space 2} .9009067{col 51}{space 1}    5.92{col 60}{space 3}0.000{col 68}{space 4} 2.489507{col 81}{space 3} 6.142237
{txt}{space 24}4  {c |}{col 28}{res}{space 2}  3.68671{col 40}{space 2} 1.211688{col 51}{space 1}    3.97{col 60}{space 3}0.000{col 68}{space 4} 1.935887{col 81}{space 3} 7.020985
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  1.63785{col 40}{space 2} .4009944{col 51}{space 1}    2.02{col 60}{space 3}0.044{col 68}{space 4} 1.013619{col 81}{space 3} 2.646511
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.760767{col 40}{space 2} .9498762{col 51}{space 1}    5.24{col 60}{space 3}0.000{col 68}{space 4} 2.292367{col 81}{space 3} 6.169767
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 5.746795{col 40}{space 2} 1.788011{col 51}{space 1}    5.62{col 60}{space 3}0.000{col 68}{space 4} 3.123128{col 81}{space 3} 10.57454
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 8.951374{col 40}{space 2}  3.54825{col 51}{space 1}    5.53{col 60}{space 3}0.000{col 68}{space 4} 4.116048{col 81}{space 3}   19.467
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.880117{col 40}{space 2} .7424203{col 51}{space 1}    4.10{col 60}{space 3}0.000{col 68}{space 4} 1.737769{col 81}{space 3} 4.773402
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 2.005378{col 40}{space 2} .4538939{col 51}{space 1}    3.07{col 60}{space 3}0.002{col 68}{space 4} 1.286876{col 81}{space 3} 3.125041
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.537502{col 40}{space 2} .3273859{col 51}{space 1}    2.02{col 60}{space 3}0.043{col 68}{space 4} 1.012899{col 81}{space 3} 2.333808
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.279718{col 40}{space 2} .3260161{col 51}{space 1}    0.97{col 60}{space 3}0.333{col 68}{space 4} .7767214{col 81}{space 3} 2.108449
{txt}{space 24}6  {c |}{col 28}{res}{space 2}  3.00985{col 40}{space 2} .6578853{col 51}{space 1}    5.04{col 60}{space 3}0.000{col 68}{space 4} 1.961062{col 81}{space 3} 4.619535
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.972006{col 40}{space 2} .5245676{col 51}{space 1}    2.55{col 60}{space 3}0.011{col 68}{space 4}   1.1708{col 81}{space 3} 3.321498
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.519631{col 40}{space 2} .6272531{col 51}{space 1}    3.71{col 60}{space 3}0.000{col 68}{space 4} 1.546797{col 81}{space 3} 4.104313
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 2.134937{col 40}{space 2}  .503135{col 51}{space 1}    3.22{col 60}{space 3}0.001{col 68}{space 4} 1.345193{col 81}{space 3} 3.388328
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.826892{col 40}{space 2} 1.162228{col 51}{space 1}    4.42{col 60}{space 3}0.000{col 68}{space 4} 2.110261{col 81}{space 3} 6.939949
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 2.083876{col 40}{space 2} .3846364{col 51}{space 1}    3.98{col 60}{space 3}0.000{col 68}{space 4} 1.451307{col 81}{space 3} 2.992158
{txt}{space 23}13  {c |}{col 28}{res}{space 2}  1.67439{col 40}{space 2} .3686312{col 51}{space 1}    2.34{col 60}{space 3}0.019{col 68}{space 4}  1.08757{col 81}{space 3} 2.577839
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.619577{col 40}{space 2} .6543348{col 51}{space 1}    3.86{col 60}{space 3}0.000{col 68}{space 4} 1.605509{col 81}{space 3}  4.27415
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.611167{col 40}{space 2} .3920957{col 51}{space 1}    1.96{col 60}{space 3}0.050{col 68}{space 4} .9999793{col 81}{space 3} 2.595912
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8503128{col 40}{space 2} .1291961{col 51}{space 1}   -1.07{col 60}{space 3}0.286{col 68}{space 4} .6313171{col 81}{space 3} 1.145275
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.613944{col 40}{space 2} .1319564{col 51}{space 1}    5.85{col 60}{space 3}0.000{col 68}{space 4} 1.374972{col 81}{space 3} 1.894449
{txt}{space 23}18  {c |}{col 28}{res}{space 2}  2.12278{col 40}{space 2} .5577227{col 51}{space 1}    2.86{col 60}{space 3}0.004{col 68}{space 4} 1.268431{col 81}{space 3} 3.552573
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7220182{col 40}{space 2}  .110357{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4} .5351138{col 81}{space 3} .9742045
{txt}{space 23}20  {c |}{col 28}{res}{space 2}  .278514{col 40}{space 2} .0847606{col 51}{space 1}   -4.20{col 60}{space 3}0.000{col 68}{space 4} .1533908{col 81}{space 3} .5057018
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .9092516{col 40}{space 2} .0796288{col 51}{space 1}   -1.09{col 60}{space 3}0.277{col 68}{space 4} .7658418{col 81}{space 3} 1.079516
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .5281249{col 40}{space 2} .1821584{col 51}{space 1}   -1.85{col 60}{space 3}0.064{col 68}{space 4} .2686236{col 81}{space 3} 1.038315
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 1.108136{col 40}{space 2} .2554865{col 51}{space 1}    0.45{col 60}{space 3}0.656{col 68}{space 4} .7052521{col 81}{space 3} 1.741174
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .2229722{col 40}{space 2} .1096165{col 51}{space 1}   -3.05{col 60}{space 3}0.002{col 68}{space 4} .0850721{col 81}{space 3} .5844053
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.670444{col 40}{space 2} .2342521{col 51}{space 1}    3.66{col 60}{space 3}0.000{col 68}{space 4}  1.26901{col 81}{space 3} 2.198867
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .7542384{col 40}{space 2} .1156247{col 51}{space 1}   -1.84{col 60}{space 3}0.066{col 68}{space 4} .5584952{col 81}{space 3} 1.018586
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.622656{col 40}{space 2} .1486927{col 51}{space 1}    5.28{col 60}{space 3}0.000{col 68}{space 4} 1.355896{col 81}{space 3} 1.941899
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.604283{col 40}{space 2} 1.122431{col 51}{space 1}    4.12{col 60}{space 3}0.000{col 68}{space 4} 1.957678{col 81}{space 3} 6.635849
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.438795{col 40}{space 2} .3820566{col 51}{space 1}    1.37{col 60}{space 3}0.171{col 68}{space 4} .8550107{col 81}{space 3} 2.421176
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.941757{col 40}{space 2} .3429413{col 51}{space 1}    3.76{col 60}{space 3}0.000{col 68}{space 4} 1.373602{col 81}{space 3} 2.744915
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.977801{col 40}{space 2} .9556875{col 51}{space 1}    5.75{col 60}{space 3}0.000{col 68}{space 4} 2.483919{col 81}{space 3} 6.370136
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.815209{col 40}{space 2} .5782566{col 51}{space 1}    1.87{col 60}{space 3}0.061{col 68}{space 4} .9722237{col 81}{space 3} 3.389121
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.569753{col 40}{space 2} .1655515{col 51}{space 1}    4.28{col 60}{space 3}0.000{col 68}{space 4} 1.276617{col 81}{space 3} 1.930198
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.742493{col 40}{space 2} .3044935{col 51}{space 1}    3.18{col 60}{space 3}0.001{col 68}{space 4} 1.237163{col 81}{space 3} 2.454229
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.810156{col 40}{space 2} .5588778{col 51}{space 1}    1.92{col 60}{space 3}0.055{col 68}{space 4} .9883514{col 81}{space 3} 3.315285
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.348117{col 40}{space 2}  .427542{col 51}{space 1}    0.94{col 60}{space 3}0.346{col 68}{space 4} .7240644{col 81}{space 3} 2.510023
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} .9543681{col 40}{space 2} .2657044{col 51}{space 1}   -0.17{col 60}{space 3}0.867{col 68}{space 4} .5530113{col 81}{space 3} 1.647016
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3270364{col 40}{space 2} .1089054{col 51}{space 1}   -3.36{col 60}{space 3}0.001{col 68}{space 4} .1702708{col 81}{space 3} .6281336
{txt}{space 23}60  {c |}{col 28}{res}{space 2} 1.045631{col 40}{space 2} .1423228{col 51}{space 1}    0.33{col 60}{space 3}0.743{col 68}{space 4} .8007921{col 81}{space 3} 1.365328
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0714071{col 40}{space 2} .0705685{col 51}{space 1}   -2.67{col 60}{space 3}0.008{col 68}{space 4} .0102929{col 81}{space 3} .4953888
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1840248{col 40}{space 2} .1165924{col 51}{space 1}   -2.67{col 60}{space 3}0.008{col 68}{space 4} .0531595{col 81}{space 3} .6370468
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6947334{col 40}{space 2} .3710089{col 51}{space 1}   -0.68{col 60}{space 3}0.495{col 68}{space 4} .2439217{col 81}{space 3} 1.978727
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2697438{col 40}{space 2} .2084199{col 51}{space 1}   -1.70{col 60}{space 3}0.090{col 68}{space 4} .0593285{col 81}{space 3} 1.226421
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4482.611{col 50}    40{col 58} 9045.222{col 69}   9235.5
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelD2
{txt}
{com}. 
. 
. *** COMPUTE Figure D2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .9934778{col 26}{space 2}  .101606{col 37}{space 1}   -0.06{col 46}{space 3}0.949{col 54}{space 4} .8130236{col 67}{space 3} 1.213985
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelD2zloyal = r(table)
{txt}
{com}. mat list modelD2zloyal
{res}
{txt}modelD2zloyal[9,1]
               (1)
     b {res}  .99347777
{txt}    se {res}  .10160605
{txt}     z {res} -.06398153
{txt}pvalue {res}  .94898493
{txt}    ll {res}  .81302358
{txt}    ul {res}  1.2139846
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** MODEL D3: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   zloyalmedian soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-633.55176}  
Iteration 2:{space 3}log pseudolikelihood = {res: -555.0074}  
Iteration 3:{space 3}log pseudolikelihood = {res:-553.89362}  
Iteration 4:{space 3}log pseudolikelihood = {res: -553.8923}  
Iteration 5:{space 3}log pseudolikelihood = {res: -553.8923}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1202.88
{txt}Log pseudolikelihood =   {res} -553.8923             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}  1.03726{col 40}{space 2} .0731798{col 51}{space 1}    0.52{col 60}{space 3}0.604{col 68}{space 4} .9033056{col 81}{space 3}  1.19108
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2}  1.04871{col 40}{space 2} .1093643{col 51}{space 1}    0.46{col 60}{space 3}0.648{col 68}{space 4} .8548465{col 81}{space 3} 1.286538
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9631342{col 40}{space 2}  .071221{col 51}{space 1}   -0.51{col 60}{space 3}0.611{col 68}{space 4} .8331879{col 81}{space 3} 1.113347
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.018245{col 40}{space 2} .0602138{col 51}{space 1}    0.31{col 60}{space 3}0.760{col 68}{space 4} .9068111{col 81}{space 3} 1.143374
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5839045{col 40}{space 2} .0525847{col 51}{space 1}   -5.97{col 60}{space 3}0.000{col 68}{space 4} .4894238{col 81}{space 3} .6966241
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.414714{col 40}{space 2} .1397817{col 51}{space 1}    3.51{col 60}{space 3}0.000{col 68}{space 4} 1.165642{col 81}{space 3} 1.717008
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.338996{col 40}{space 2} .1322919{col 51}{space 1}    2.95{col 60}{space 3}0.003{col 68}{space 4} 1.103268{col 81}{space 3}  1.62509
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2}  1.02394{col 40}{space 2}  .171291{col 51}{space 1}    0.14{col 60}{space 3}0.888{col 68}{space 4} .7377011{col 81}{space 3} 1.421245
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .8250791{col 40}{space 2} .0878383{col 51}{space 1}   -1.81{col 60}{space 3}0.071{col 68}{space 4} .6696938{col 81}{space 3} 1.016518
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0014742{col 40}{space 2} .0036993{col 51}{space 1}   -2.60{col 60}{space 3}0.009{col 68}{space 4} .0000108{col 81}{space 3} .2015822
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.748735{col 40}{space 2} .3880577{col 51}{space 1}    2.52{col 60}{space 3}0.012{col 68}{space 4} 1.131973{col 81}{space 3} 2.701545
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2060316{col 40}{space 2} .0376685{col 51}{space 1}   -8.64{col 60}{space 3}0.000{col 68}{space 4} .1439824{col 81}{space 3} .2948207
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9967251{col 40}{space 2} .0034721{col 51}{space 1}   -0.94{col 60}{space 3}0.346{col 68}{space 4}  .989943{col 81}{space 3} 1.003554
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9336716{col 40}{space 2}  .041934{col 51}{space 1}   -1.53{col 60}{space 3}0.126{col 68}{space 4} .8549961{col 81}{space 3} 1.019587
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.802281{col 40}{space 2} .3499338{col 51}{space 1}    3.03{col 60}{space 3}0.002{col 68}{space 4} 1.231833{col 81}{space 3} 2.636897
{txt}{space 24}3  {c |}{col 28}{res}{space 2}   5.7625{col 40}{space 2} .7923468{col 51}{space 1}   12.74{col 60}{space 3}0.000{col 68}{space 4} 4.401191{col 81}{space 3} 7.544867
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.788719{col 40}{space 2} 1.394486{col 51}{space 1}    5.38{col 60}{space 3}0.000{col 68}{space 4} 2.706123{col 81}{space 3} 8.474053
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.300596{col 40}{space 2}  .163624{col 51}{space 1}    2.09{col 60}{space 3}0.037{col 68}{space 4} 1.016379{col 81}{space 3} 1.664292
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.810487{col 40}{space 2} .3370708{col 51}{space 1}    8.62{col 60}{space 3}0.000{col 68}{space 4} 2.221746{col 81}{space 3} 3.555241
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 5.528511{col 40}{space 2} 1.271797{col 51}{space 1}    7.43{col 60}{space 3}0.000{col 68}{space 4} 3.522046{col 81}{space 3} 8.678034
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 8.743078{col 40}{space 2} 2.506552{col 51}{space 1}    7.56{col 60}{space 3}0.000{col 68}{space 4} 4.984636{col 81}{space 3}  15.3354
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 2.39e-06{col 40}{space 2} 4.52e-06{col 51}{space 1}   -6.85{col 60}{space 3}0.000{col 68}{space 4} 5.89e-08{col 81}{space 3} .0000971
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2}  .898078{col 40}{space 2} .0330441{col 51}{space 1}   27.18{col 60}{space 3}0.000{col 68}{space 4} .8333128{col 81}{space 3} .9628432
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2}  2.45488{col 40}{space 2} .0811192{col 68}{space 4} 2.300929{col 81}{space 3} 2.619133
{txt}                       1/p {c |}{col 28}{res}{space 2} .4073518{col 40}{space 2} .0134606{col 68}{space 4} .3818058{col 81}{space 3} .4346071
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-553.8923{col 50}    23{col 58} 1153.785{col 69} 1263.194
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelD3
{txt}
{com}. 
. 
. *** COMPUTE Figure D2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.051216{col 26}{space 2} .1012583{col 37}{space 1}    0.52{col 46}{space 3}0.604{col 54}{space 4} .8703624{col 67}{space 3}  1.26965
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelD3zloyal = r(table)
{txt}
{com}. mat list modelD3zloyal
{res}
{txt}modelD3zloyal[9,1]
              (1)
     b {res} 1.0512162
{txt}    se {res} .10125829
{txt}     z {res} .51853415
{txt}pvalue {res} .60408564
{txt}    ll {res} .87036241
{txt}    ul {res} 1.2696498
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure D3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Re-Estimate Model D3  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-633.55176}  
Iteration 2:{space 3}log pseudolikelihood = {res: -555.0074}  
Iteration 3:{space 3}log pseudolikelihood = {res:-553.89362}  
Iteration 4:{space 3}log pseudolikelihood = {res: -553.8923}  
Iteration 5:{space 3}log pseudolikelihood = {res: -553.8923}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1202.88
{txt}Log pseudolikelihood =   {res} -553.8923             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}  1.03726{col 40}{space 2} .0731798{col 51}{space 1}    0.52{col 60}{space 3}0.604{col 68}{space 4} .9033056{col 81}{space 3}  1.19108
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2}  1.04871{col 40}{space 2} .1093643{col 51}{space 1}    0.46{col 60}{space 3}0.648{col 68}{space 4} .8548465{col 81}{space 3} 1.286538
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9631342{col 40}{space 2}  .071221{col 51}{space 1}   -0.51{col 60}{space 3}0.611{col 68}{space 4} .8331879{col 81}{space 3} 1.113347
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.018245{col 40}{space 2} .0602138{col 51}{space 1}    0.31{col 60}{space 3}0.760{col 68}{space 4} .9068111{col 81}{space 3} 1.143374
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5839045{col 40}{space 2} .0525847{col 51}{space 1}   -5.97{col 60}{space 3}0.000{col 68}{space 4} .4894238{col 81}{space 3} .6966241
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.414714{col 40}{space 2} .1397817{col 51}{space 1}    3.51{col 60}{space 3}0.000{col 68}{space 4} 1.165642{col 81}{space 3} 1.717008
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.338996{col 40}{space 2} .1322919{col 51}{space 1}    2.95{col 60}{space 3}0.003{col 68}{space 4} 1.103268{col 81}{space 3}  1.62509
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2}  1.02394{col 40}{space 2}  .171291{col 51}{space 1}    0.14{col 60}{space 3}0.888{col 68}{space 4} .7377011{col 81}{space 3} 1.421245
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .8250791{col 40}{space 2} .0878383{col 51}{space 1}   -1.81{col 60}{space 3}0.071{col 68}{space 4} .6696938{col 81}{space 3} 1.016518
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0014742{col 40}{space 2} .0036993{col 51}{space 1}   -2.60{col 60}{space 3}0.009{col 68}{space 4} .0000108{col 81}{space 3} .2015822
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.748735{col 40}{space 2} .3880577{col 51}{space 1}    2.52{col 60}{space 3}0.012{col 68}{space 4} 1.131973{col 81}{space 3} 2.701545
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2060316{col 40}{space 2} .0376685{col 51}{space 1}   -8.64{col 60}{space 3}0.000{col 68}{space 4} .1439824{col 81}{space 3} .2948207
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9967251{col 40}{space 2} .0034721{col 51}{space 1}   -0.94{col 60}{space 3}0.346{col 68}{space 4}  .989943{col 81}{space 3} 1.003554
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9336716{col 40}{space 2}  .041934{col 51}{space 1}   -1.53{col 60}{space 3}0.126{col 68}{space 4} .8549961{col 81}{space 3} 1.019587
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.802281{col 40}{space 2} .3499338{col 51}{space 1}    3.03{col 60}{space 3}0.002{col 68}{space 4} 1.231833{col 81}{space 3} 2.636897
{txt}{space 24}3  {c |}{col 28}{res}{space 2}   5.7625{col 40}{space 2} .7923468{col 51}{space 1}   12.74{col 60}{space 3}0.000{col 68}{space 4} 4.401191{col 81}{space 3} 7.544867
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.788719{col 40}{space 2} 1.394486{col 51}{space 1}    5.38{col 60}{space 3}0.000{col 68}{space 4} 2.706123{col 81}{space 3} 8.474053
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.300596{col 40}{space 2}  .163624{col 51}{space 1}    2.09{col 60}{space 3}0.037{col 68}{space 4} 1.016379{col 81}{space 3} 1.664292
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.810487{col 40}{space 2} .3370708{col 51}{space 1}    8.62{col 60}{space 3}0.000{col 68}{space 4} 2.221746{col 81}{space 3} 3.555241
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 5.528511{col 40}{space 2} 1.271797{col 51}{space 1}    7.43{col 60}{space 3}0.000{col 68}{space 4} 3.522046{col 81}{space 3} 8.678034
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 8.743078{col 40}{space 2} 2.506552{col 51}{space 1}    7.56{col 60}{space 3}0.000{col 68}{space 4} 4.984636{col 81}{space 3}  15.3354
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 2.39e-06{col 40}{space 2} 4.52e-06{col 51}{space 1}   -6.85{col 60}{space 3}0.000{col 68}{space 4} 5.89e-08{col 81}{space 3} .0000971
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2}  .898078{col 40}{space 2} .0330441{col 51}{space 1}   27.18{col 60}{space 3}0.000{col 68}{space 4} .8333128{col 81}{space 3} .9628432
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2}  2.45488{col 40}{space 2} .0811192{col 68}{space 4} 2.300929{col 81}{space 3} 2.619133
{txt}                       1/p {c |}{col 28}{res}{space 2} .4073518{col 40}{space 2} .0134606{col 68}{space 4} .3818058{col 81}{space 3} .4346071
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelD3a
{txt}
{com}. 
. 
. margins, predict(median time) at(zloyalmedian=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 990.7759{col 26}{space 2} 25.47006{col 37}{space 1}   38.90{col 46}{space 3}0.000{col 54}{space 4} 940.8555{col 67}{space 3} 1040.696
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  970.821{col 26}{space 2} 24.61092{col 37}{space 1}   39.45{col 46}{space 3}0.000{col 54}{space 4} 922.5845{col 67}{space 3} 1019.058
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential  **
. margins, predict(median time) at(zloyalmedian=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.27{col 38}{space 2}   0.6049
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}-19.95494{col 26}{space 2} 38.56558{col 37}{space 5}-95.54209{col 51}{space 3} 55.63221
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelD3azloyal = r(table)
{txt}
{com}. mat list modelD3azloyal
{res}
{txt}modelD3azloyal[9,1]
             r2vs1.
               _at
     b {res} -19.954939
{txt}    se {res}   38.56558
{txt}     z {res} -.51742873
{txt}pvalue {res}  .60485691
{txt}    ll {res} -95.542088
{txt}    ul {res}  55.632209
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. estimates restore modelD3a
{txt}(results {stata estimates replay modelD3a:modelD3a} are active now)

{com}. 
. margins, predict(median time) at(zloyalmedian=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 994.4611{col 26}{space 2} 31.41095{col 37}{space 1}   31.66{col 46}{space 3}0.000{col 54}{space 4} 932.8967{col 67}{space 3} 1056.025
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 960.1445{col 26}{space 2} 42.17426{col 37}{space 1}   22.77{col 46}{space 3}0.000{col 54}{space 4} 877.4844{col 67}{space 3} 1042.804
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(zloyalmedian=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.27{col 38}{space 2}   0.6036
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}-34.31661{col 26}{space 2} 66.08242{col 37}{space 5}-163.8358{col 51}{space 3} 95.20254
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelD3bzloyal = r(table)
{txt}
{com}. mat list modelD3bzloyal
{res}
{txt}modelD3bzloyal[9,1]
             r2vs1.
               _at
     b {res} -34.316612
{txt}    se {res}  66.082417
{txt}     z {res}  -.5193002
{txt}pvalue {res}  .60355141
{txt}    ll {res} -163.83577
{txt}    ul {res}  95.202544
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL D4: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   zloyalmedian  soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-610.50073}  
Iteration 2:{space 3}log pseudolikelihood = {res:-510.39421}  
Iteration 3:{space 3}log pseudolikelihood = {res:   -509.14}  
Iteration 4:{space 3}log pseudolikelihood = {res:-509.13741}  
Iteration 5:{space 3}log pseudolikelihood = {res:-509.13741}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(21)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-509.13741             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} .9975805{col 40}{space 2} .0761989{col 51}{space 1}   -0.03{col 60}{space 3}0.975{col 68}{space 4}  .858875{col 81}{space 3} 1.158686
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.052716{col 40}{space 2} .1803565{col 51}{space 1}    0.30{col 60}{space 3}0.764{col 68}{space 4} .7524525{col 81}{space 3} 1.472798
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.045101{col 40}{space 2}  .081496{col 51}{space 1}    0.57{col 60}{space 3}0.572{col 68}{space 4} .8969788{col 81}{space 3} 1.217682
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9791656{col 40}{space 2} .0631778{col 51}{space 1}   -0.33{col 60}{space 3}0.744{col 68}{space 4} .8628492{col 81}{space 3} 1.111162
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5260913{col 40}{space 2} .0561352{col 51}{space 1}   -6.02{col 60}{space 3}0.000{col 68}{space 4} .4268113{col 81}{space 3} .6484649
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .7136474{col 40}{space 2} .1667896{col 51}{space 1}   -1.44{col 60}{space 3}0.149{col 68}{space 4} .4513836{col 81}{space 3} 1.128292
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .6839884{col 40}{space 2} .1648132{col 51}{space 1}   -1.58{col 60}{space 3}0.115{col 68}{space 4} .4265246{col 81}{space 3} 1.096866
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.425527{col 40}{space 2} .2232514{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} 1.048743{col 81}{space 3} 1.937679
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2}  1.65193{col 40}{space 2} .3981231{col 51}{space 1}    2.08{col 60}{space 3}0.037{col 68}{space 4} 1.030026{col 81}{space 3} 2.649325
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 2.93e-10{col 40}{space 2} 3.04e-09{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4} 4.24e-19{col 81}{space 3} .2016981
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2}  458.031{col 40}{space 2} 1070.405{col 51}{space 1}    2.62{col 60}{space 3}0.009{col 68}{space 4} 4.695293{col 81}{space 3} 44681.43
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1794696{col 40}{space 2} .0396378{col 51}{space 1}   -7.78{col 60}{space 3}0.000{col 68}{space 4}  .116411{col 81}{space 3} .2766864
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9925571{col 40}{space 2} .0047209{col 51}{space 1}   -1.57{col 60}{space 3}0.116{col 68}{space 4} .9833473{col 81}{space 3} 1.001853
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2}  1.11976{col 40}{space 2}   .10682{col 51}{space 1}    1.19{col 60}{space 3}0.236{col 68}{space 4} .9288042{col 81}{space 3} 1.349975
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.679792{col 40}{space 2} .3736896{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.086164{col 81}{space 3} 2.597857
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.365078{col 40}{space 2} .9564706{col 51}{space 1}    6.73{col 60}{space 3}0.000{col 68}{space 4} 2.841042{col 81}{space 3} 6.706659
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.096588{col 40}{space 2} 1.262068{col 51}{space 1}    4.58{col 60}{space 3}0.000{col 68}{space 4}  2.23968{col 81}{space 3} 7.493051
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.528742{col 40}{space 2} .3843982{col 51}{space 1}    1.69{col 60}{space 3}0.091{col 68}{space 4} .9339026{col 81}{space 3} 2.502457
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.509746{col 40}{space 2} .9007274{col 51}{space 1}    4.89{col 60}{space 3}0.000{col 68}{space 4} 2.122397{col 81}{space 3} 5.803964
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.380559{col 40}{space 2} 1.948788{col 51}{space 1}    6.07{col 60}{space 3}0.000{col 68}{space 4} 3.506548{col 81}{space 3} 11.61015
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 9.937148{col 40}{space 2} 3.903017{col 51}{space 1}    5.85{col 60}{space 3}0.000{col 68}{space 4} 4.601876{col 81}{space 3} 21.45796
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.552413{col 40}{space 2} .6155247{col 51}{space 1}    3.89{col 60}{space 3}0.000{col 68}{space 4} 1.591037{col 81}{space 3} 4.094696
{txt}{space 24}3  {c |}{col 28}{res}{space 2}  1.78076{col 40}{space 2} .3894788{col 51}{space 1}    2.64{col 60}{space 3}0.008{col 68}{space 4} 1.159938{col 81}{space 3} 2.733857
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.412731{col 40}{space 2} .2747163{col 51}{space 1}    1.78{col 60}{space 3}0.076{col 68}{space 4} .9650215{col 81}{space 3} 2.068151
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  1.17137{col 40}{space 2} .2832236{col 51}{space 1}    0.65{col 60}{space 3}0.513{col 68}{space 4} .7292615{col 81}{space 3} 1.881502
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.643994{col 40}{space 2} .5642289{col 51}{space 1}    4.56{col 60}{space 3}0.000{col 68}{space 4} 1.740259{col 81}{space 3} 4.017049
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.797097{col 40}{space 2} .4576315{col 51}{space 1}    2.30{col 60}{space 3}0.021{col 68}{space 4}  1.09097{col 81}{space 3} 2.960263
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.269572{col 40}{space 2} .5246663{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.442674{col 81}{space 3} 3.570423
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 1.963689{col 40}{space 2} .4341662{col 51}{space 1}    3.05{col 60}{space 3}0.002{col 68}{space 4} 1.273135{col 81}{space 3} 3.028803
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.286558{col 40}{space 2} .9680923{col 51}{space 1}    4.04{col 60}{space 3}0.000{col 68}{space 4} 1.845059{col 81}{space 3} 5.854263
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.939172{col 40}{space 2} .3359188{col 51}{space 1}    3.82{col 60}{space 3}0.000{col 68}{space 4} 1.380907{col 81}{space 3} 2.723129
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.492997{col 40}{space 2} .3074361{col 51}{space 1}    1.95{col 60}{space 3}0.052{col 68}{space 4} .9971959{col 81}{space 3} 2.235307
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.251827{col 40}{space 2} .5340609{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} 1.414678{col 81}{space 3} 3.584369
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.493269{col 40}{space 2} .3404951{col 51}{space 1}    1.76{col 60}{space 3}0.079{col 68}{space 4} .9550966{col 81}{space 3} 2.334689
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8379345{col 40}{space 2}  .135301{col 51}{space 1}   -1.10{col 60}{space 3}0.274{col 68}{space 4} .6106141{col 81}{space 3} 1.149882
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.613209{col 40}{space 2} .1353603{col 51}{space 1}    5.70{col 60}{space 3}0.000{col 68}{space 4} 1.368574{col 81}{space 3} 1.901572
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 1.899022{col 40}{space 2} .4667259{col 51}{space 1}    2.61{col 60}{space 3}0.009{col 68}{space 4} 1.173083{col 81}{space 3} 3.074194
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7233787{col 40}{space 2} .1081695{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}  .539613{col 81}{space 3} .9697262
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3272085{col 40}{space 2} .0916201{col 51}{space 1}   -3.99{col 60}{space 3}0.000{col 68}{space 4} .1890095{col 81}{space 3} .5664551
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .9456643{col 40}{space 2}  .090427{col 51}{space 1}   -0.58{col 60}{space 3}0.559{col 68}{space 4} .7840482{col 81}{space 3} 1.140594
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .5749936{col 40}{space 2}  .186071{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4} .3049367{col 81}{space 3} 1.084217
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 1.260247{col 40}{space 2} .2910528{col 51}{space 1}    1.00{col 60}{space 3}0.317{col 68}{space 4} .8014406{col 81}{space 3} 1.981708
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .2464082{col 40}{space 2} .0966967{col 51}{space 1}   -3.57{col 60}{space 3}0.000{col 68}{space 4} .1141886{col 81}{space 3} .5317258
{txt}{space 23}25  {c |}{col 28}{res}{space 2}  1.75156{col 40}{space 2} .2666495{col 51}{space 1}    3.68{col 60}{space 3}0.000{col 68}{space 4} 1.299697{col 81}{space 3} 2.360522
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .7620235{col 40}{space 2} .1297052{col 51}{space 1}   -1.60{col 60}{space 3}0.110{col 68}{space 4} .5458635{col 81}{space 3} 1.063782
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.456989{col 40}{space 2} .1364901{col 51}{space 1}    4.02{col 60}{space 3}0.000{col 68}{space 4} 1.212596{col 81}{space 3} 1.750638
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.108887{col 40}{space 2} .8905512{col 51}{space 1}    3.96{col 60}{space 3}0.000{col 68}{space 4} 1.773272{col 81}{space 3} 5.450477
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.291461{col 40}{space 2} .3368383{col 51}{space 1}    0.98{col 60}{space 3}0.327{col 68}{space 4} .7745887{col 81}{space 3} 2.153234
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.773065{col 40}{space 2} .3025499{col 51}{space 1}    3.36{col 60}{space 3}0.001{col 68}{space 4} 1.269049{col 81}{space 3} 2.477256
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.497966{col 40}{space 2} .7852574{col 51}{space 1}    5.58{col 60}{space 3}0.000{col 68}{space 4} 2.252836{col 81}{space 3} 5.431273
{txt}{space 23}52  {c |}{col 28}{res}{space 2}  1.82827{col 40}{space 2} .5672493{col 51}{space 1}    1.94{col 60}{space 3}0.052{col 68}{space 4} .9952722{col 81}{space 3}  3.35845
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.532951{col 40}{space 2} .1707518{col 51}{space 1}    3.84{col 60}{space 3}0.000{col 68}{space 4} 1.232295{col 81}{space 3}  1.90696
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.576217{col 40}{space 2} .2661745{col 51}{space 1}    2.69{col 60}{space 3}0.007{col 68}{space 4} 1.132073{col 81}{space 3} 2.194612
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.513768{col 40}{space 2} .4396908{col 51}{space 1}    1.43{col 60}{space 3}0.153{col 68}{space 4} .8566794{col 81}{space 3} 2.674855
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.281511{col 40}{space 2} .3922628{col 51}{space 1}    0.81{col 60}{space 3}0.418{col 68}{space 4} .7033547{col 81}{space 3} 2.334909
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} .7881815{col 40}{space 2} .2328378{col 51}{space 1}   -0.81{col 60}{space 3}0.420{col 68}{space 4}  .441745{col 81}{space 3} 1.406309
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3433831{col 40}{space 2} .0708196{col 51}{space 1}   -5.18{col 60}{space 3}0.000{col 68}{space 4} .2292062{col 81}{space 3} .5144361
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .8916051{col 40}{space 2} .1212462{col 51}{space 1}   -0.84{col 60}{space 3}0.399{col 68}{space 4}     .683{col 81}{space 3} 1.163923
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0806415{col 40}{space 2} .0793458{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4} .0117229{col 81}{space 3} .5547326
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1893128{col 40}{space 2} .1193253{col 51}{space 1}   -2.64{col 60}{space 3}0.008{col 68}{space 4} .0550378{col 81}{space 3} .6511767
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6684775{col 40}{space 2} .3618493{col 51}{space 1}   -0.74{col 60}{space 3}0.457{col 68}{space 4} .2313814{col 81}{space 3}  1.93128
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2742909{col 40}{space 2} .2111639{col 51}{space 1}   -1.68{col 60}{space 3}0.093{col 68}{space 4} .0606612{col 81}{space 3} 1.240258
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0002703{col 40}{space 2} .0014546{col 51}{space 1}   -1.53{col 60}{space 3}0.127{col 68}{space 4} 7.09e-09{col 81}{space 3}  10.3025
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9745569{col 40}{space 2} .0311706{col 51}{space 1}   31.27{col 60}{space 3}0.000{col 68}{space 4} .9134637{col 81}{space 3}  1.03565
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.649993{col 40}{space 2} .0826018{col 68}{space 4} 2.492942{col 81}{space 3} 2.816937
{txt}                       1/p {c |}{col 28}{res}{space 2} .3773595{col 40}{space 2} .0117625{col 68}{space 4} .3549955{col 81}{space 3} .4011324
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-509.1374{col 50}    23{col 58} 1064.275{col 69} 1173.684
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modeld4
{txt}
{com}. 
. 
. 
. *** COMPUTE Figure D2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. estimates restore modeld4
{txt}(results {stata estimates replay modeld4:modeld4} are active now)

{com}. 
. *there was an interaction in the lincomest between soubinaryagency2nom and zloyalmedian didn't seem to be needed based on the above code
. 
. lincomest c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .996698{col 26}{space 2} .1039441{col 37}{space 1}   -0.03{col 46}{space 3}0.975{col 54}{space 4} .8124433{col 67}{space 3}  1.22274
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelD4zloyal = r(table)
{txt}
{com}. mat list modelD4zloyal
{res}
{txt}modelD4zloyal[9,1]
               (1)
     b {res}  .99669801
{txt}    se {res}  .10394412
{txt}     z {res} -.03171446
{txt}pvalue {res}  .97469977
{txt}    ll {res}  .81244332
{txt}    ul {res}    1.22274
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure D3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}ADDITIVE EFFECT{c )-} {c -(}{c -(}4 [MD1−MD4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Re-Estimate Model D4  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom   zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-610.50073}  
Iteration 2:{space 3}log pseudolikelihood = {res:-510.39421}  
Iteration 3:{space 3}log pseudolikelihood = {res:   -509.14}  
Iteration 4:{space 3}log pseudolikelihood = {res:-509.13741}  
Iteration 5:{space 3}log pseudolikelihood = {res:-509.13741}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(21)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-509.13741             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} .9975805{col 40}{space 2} .0761989{col 51}{space 1}   -0.03{col 60}{space 3}0.975{col 68}{space 4}  .858875{col 81}{space 3} 1.158686
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.052716{col 40}{space 2} .1803565{col 51}{space 1}    0.30{col 60}{space 3}0.764{col 68}{space 4} .7524525{col 81}{space 3} 1.472798
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.045101{col 40}{space 2}  .081496{col 51}{space 1}    0.57{col 60}{space 3}0.572{col 68}{space 4} .8969788{col 81}{space 3} 1.217682
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9791656{col 40}{space 2} .0631778{col 51}{space 1}   -0.33{col 60}{space 3}0.744{col 68}{space 4} .8628492{col 81}{space 3} 1.111162
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5260913{col 40}{space 2} .0561352{col 51}{space 1}   -6.02{col 60}{space 3}0.000{col 68}{space 4} .4268113{col 81}{space 3} .6484649
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .7136474{col 40}{space 2} .1667896{col 51}{space 1}   -1.44{col 60}{space 3}0.149{col 68}{space 4} .4513836{col 81}{space 3} 1.128292
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .6839884{col 40}{space 2} .1648132{col 51}{space 1}   -1.58{col 60}{space 3}0.115{col 68}{space 4} .4265246{col 81}{space 3} 1.096866
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.425527{col 40}{space 2} .2232514{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} 1.048743{col 81}{space 3} 1.937679
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2}  1.65193{col 40}{space 2} .3981231{col 51}{space 1}    2.08{col 60}{space 3}0.037{col 68}{space 4} 1.030026{col 81}{space 3} 2.649325
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 2.93e-10{col 40}{space 2} 3.04e-09{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4} 4.24e-19{col 81}{space 3} .2016981
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2}  458.031{col 40}{space 2} 1070.405{col 51}{space 1}    2.62{col 60}{space 3}0.009{col 68}{space 4} 4.695293{col 81}{space 3} 44681.43
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1794696{col 40}{space 2} .0396378{col 51}{space 1}   -7.78{col 60}{space 3}0.000{col 68}{space 4}  .116411{col 81}{space 3} .2766864
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9925571{col 40}{space 2} .0047209{col 51}{space 1}   -1.57{col 60}{space 3}0.116{col 68}{space 4} .9833473{col 81}{space 3} 1.001853
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2}  1.11976{col 40}{space 2}   .10682{col 51}{space 1}    1.19{col 60}{space 3}0.236{col 68}{space 4} .9288042{col 81}{space 3} 1.349975
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.679792{col 40}{space 2} .3736896{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.086164{col 81}{space 3} 2.597857
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.365078{col 40}{space 2} .9564706{col 51}{space 1}    6.73{col 60}{space 3}0.000{col 68}{space 4} 2.841042{col 81}{space 3} 6.706659
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 4.096588{col 40}{space 2} 1.262068{col 51}{space 1}    4.58{col 60}{space 3}0.000{col 68}{space 4}  2.23968{col 81}{space 3} 7.493051
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.528742{col 40}{space 2} .3843982{col 51}{space 1}    1.69{col 60}{space 3}0.091{col 68}{space 4} .9339026{col 81}{space 3} 2.502457
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.509746{col 40}{space 2} .9007274{col 51}{space 1}    4.89{col 60}{space 3}0.000{col 68}{space 4} 2.122397{col 81}{space 3} 5.803964
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.380559{col 40}{space 2} 1.948788{col 51}{space 1}    6.07{col 60}{space 3}0.000{col 68}{space 4} 3.506548{col 81}{space 3} 11.61015
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 9.937148{col 40}{space 2} 3.903017{col 51}{space 1}    5.85{col 60}{space 3}0.000{col 68}{space 4} 4.601876{col 81}{space 3} 21.45796
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.552413{col 40}{space 2} .6155247{col 51}{space 1}    3.89{col 60}{space 3}0.000{col 68}{space 4} 1.591037{col 81}{space 3} 4.094696
{txt}{space 24}3  {c |}{col 28}{res}{space 2}  1.78076{col 40}{space 2} .3894788{col 51}{space 1}    2.64{col 60}{space 3}0.008{col 68}{space 4} 1.159938{col 81}{space 3} 2.733857
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.412731{col 40}{space 2} .2747163{col 51}{space 1}    1.78{col 60}{space 3}0.076{col 68}{space 4} .9650215{col 81}{space 3} 2.068151
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  1.17137{col 40}{space 2} .2832236{col 51}{space 1}    0.65{col 60}{space 3}0.513{col 68}{space 4} .7292615{col 81}{space 3} 1.881502
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.643994{col 40}{space 2} .5642289{col 51}{space 1}    4.56{col 60}{space 3}0.000{col 68}{space 4} 1.740259{col 81}{space 3} 4.017049
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.797097{col 40}{space 2} .4576315{col 51}{space 1}    2.30{col 60}{space 3}0.021{col 68}{space 4}  1.09097{col 81}{space 3} 2.960263
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.269572{col 40}{space 2} .5246663{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.442674{col 81}{space 3} 3.570423
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 1.963689{col 40}{space 2} .4341662{col 51}{space 1}    3.05{col 60}{space 3}0.002{col 68}{space 4} 1.273135{col 81}{space 3} 3.028803
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.286558{col 40}{space 2} .9680923{col 51}{space 1}    4.04{col 60}{space 3}0.000{col 68}{space 4} 1.845059{col 81}{space 3} 5.854263
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.939172{col 40}{space 2} .3359188{col 51}{space 1}    3.82{col 60}{space 3}0.000{col 68}{space 4} 1.380907{col 81}{space 3} 2.723129
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.492997{col 40}{space 2} .3074361{col 51}{space 1}    1.95{col 60}{space 3}0.052{col 68}{space 4} .9971959{col 81}{space 3} 2.235307
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.251827{col 40}{space 2} .5340609{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} 1.414678{col 81}{space 3} 3.584369
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.493269{col 40}{space 2} .3404951{col 51}{space 1}    1.76{col 60}{space 3}0.079{col 68}{space 4} .9550966{col 81}{space 3} 2.334689
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8379345{col 40}{space 2}  .135301{col 51}{space 1}   -1.10{col 60}{space 3}0.274{col 68}{space 4} .6106141{col 81}{space 3} 1.149882
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.613209{col 40}{space 2} .1353603{col 51}{space 1}    5.70{col 60}{space 3}0.000{col 68}{space 4} 1.368574{col 81}{space 3} 1.901572
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 1.899022{col 40}{space 2} .4667259{col 51}{space 1}    2.61{col 60}{space 3}0.009{col 68}{space 4} 1.173083{col 81}{space 3} 3.074194
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7233787{col 40}{space 2} .1081695{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}  .539613{col 81}{space 3} .9697262
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3272085{col 40}{space 2} .0916201{col 51}{space 1}   -3.99{col 60}{space 3}0.000{col 68}{space 4} .1890095{col 81}{space 3} .5664551
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .9456643{col 40}{space 2}  .090427{col 51}{space 1}   -0.58{col 60}{space 3}0.559{col 68}{space 4} .7840482{col 81}{space 3} 1.140594
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .5749936{col 40}{space 2}  .186071{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4} .3049367{col 81}{space 3} 1.084217
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 1.260247{col 40}{space 2} .2910528{col 51}{space 1}    1.00{col 60}{space 3}0.317{col 68}{space 4} .8014406{col 81}{space 3} 1.981708
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .2464082{col 40}{space 2} .0966967{col 51}{space 1}   -3.57{col 60}{space 3}0.000{col 68}{space 4} .1141886{col 81}{space 3} .5317258
{txt}{space 23}25  {c |}{col 28}{res}{space 2}  1.75156{col 40}{space 2} .2666495{col 51}{space 1}    3.68{col 60}{space 3}0.000{col 68}{space 4} 1.299697{col 81}{space 3} 2.360522
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .7620235{col 40}{space 2} .1297052{col 51}{space 1}   -1.60{col 60}{space 3}0.110{col 68}{space 4} .5458635{col 81}{space 3} 1.063782
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.456989{col 40}{space 2} .1364901{col 51}{space 1}    4.02{col 60}{space 3}0.000{col 68}{space 4} 1.212596{col 81}{space 3} 1.750638
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.108887{col 40}{space 2} .8905512{col 51}{space 1}    3.96{col 60}{space 3}0.000{col 68}{space 4} 1.773272{col 81}{space 3} 5.450477
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.291461{col 40}{space 2} .3368383{col 51}{space 1}    0.98{col 60}{space 3}0.327{col 68}{space 4} .7745887{col 81}{space 3} 2.153234
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.773065{col 40}{space 2} .3025499{col 51}{space 1}    3.36{col 60}{space 3}0.001{col 68}{space 4} 1.269049{col 81}{space 3} 2.477256
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.497966{col 40}{space 2} .7852574{col 51}{space 1}    5.58{col 60}{space 3}0.000{col 68}{space 4} 2.252836{col 81}{space 3} 5.431273
{txt}{space 23}52  {c |}{col 28}{res}{space 2}  1.82827{col 40}{space 2} .5672493{col 51}{space 1}    1.94{col 60}{space 3}0.052{col 68}{space 4} .9952722{col 81}{space 3}  3.35845
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.532951{col 40}{space 2} .1707518{col 51}{space 1}    3.84{col 60}{space 3}0.000{col 68}{space 4} 1.232295{col 81}{space 3}  1.90696
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.576217{col 40}{space 2} .2661745{col 51}{space 1}    2.69{col 60}{space 3}0.007{col 68}{space 4} 1.132073{col 81}{space 3} 2.194612
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.513768{col 40}{space 2} .4396908{col 51}{space 1}    1.43{col 60}{space 3}0.153{col 68}{space 4} .8566794{col 81}{space 3} 2.674855
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.281511{col 40}{space 2} .3922628{col 51}{space 1}    0.81{col 60}{space 3}0.418{col 68}{space 4} .7033547{col 81}{space 3} 2.334909
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} .7881815{col 40}{space 2} .2328378{col 51}{space 1}   -0.81{col 60}{space 3}0.420{col 68}{space 4}  .441745{col 81}{space 3} 1.406309
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3433831{col 40}{space 2} .0708196{col 51}{space 1}   -5.18{col 60}{space 3}0.000{col 68}{space 4} .2292062{col 81}{space 3} .5144361
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .8916051{col 40}{space 2} .1212462{col 51}{space 1}   -0.84{col 60}{space 3}0.399{col 68}{space 4}     .683{col 81}{space 3} 1.163923
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0806415{col 40}{space 2} .0793458{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4} .0117229{col 81}{space 3} .5547326
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1893128{col 40}{space 2} .1193253{col 51}{space 1}   -2.64{col 60}{space 3}0.008{col 68}{space 4} .0550378{col 81}{space 3} .6511767
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6684775{col 40}{space 2} .3618493{col 51}{space 1}   -0.74{col 60}{space 3}0.457{col 68}{space 4} .2313814{col 81}{space 3}  1.93128
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2742909{col 40}{space 2} .2111639{col 51}{space 1}   -1.68{col 60}{space 3}0.093{col 68}{space 4} .0606612{col 81}{space 3} 1.240258
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0002703{col 40}{space 2} .0014546{col 51}{space 1}   -1.53{col 60}{space 3}0.127{col 68}{space 4} 7.09e-09{col 81}{space 3}  10.3025
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9745569{col 40}{space 2} .0311706{col 51}{space 1}   31.27{col 60}{space 3}0.000{col 68}{space 4} .9134637{col 81}{space 3}  1.03565
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.649993{col 40}{space 2} .0826018{col 68}{space 4} 2.492942{col 81}{space 3} 2.816937
{txt}                       1/p {c |}{col 28}{res}{space 2} .3773595{col 40}{space 2} .0117625{col 68}{space 4} .3549955{col 81}{space 3} .4011324
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelD4a
{txt}
{com}. 
. margins, predict(median time) at(zloyalmedian=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 999.7964{col 26}{space 2} 24.38049{col 37}{space 1}   41.01{col 46}{space 3}0.000{col 54}{space 4} 952.0115{col 67}{space 3} 1047.581
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1001.045{col 26}{space 2} 27.71301{col 37}{space 1}   36.12{col 46}{space 3}0.000{col 54}{space 4} 946.7285{col 67}{space 3} 1055.362
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(zloyalmedian=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.00{col 38}{space 2}   0.9747
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 1.248624{col 26}{space 2} 39.37551{col 37}{space 5}-75.92596{col 51}{space 3} 78.42321
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelD4azloyal = r(table)
{txt}
{com}. mat list modelD4azloyal
{res}
{txt}modelD4azloyal[9,1]
             r2vs1.
               _at
     b {res}  1.2486237
{txt}    se {res}  39.375511
{txt}     z {res}  .03171067
{txt}pvalue {res}  .97470279
{txt}    ll {res} -75.925959
{txt}    ul {res}  78.423206
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. estimates restore modelD4a
{txt}(results {stata estimates replay modelD4a:modelD4a} are active now)

{com}. 
. margins, predict(median time) at(zloyalmedian=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 999.5688{col 26}{space 2} 29.94549{col 37}{space 1}   33.38{col 46}{space 3}0.000{col 54}{space 4} 940.8767{col 67}{space 3} 1058.261
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1001.724{col 26}{space 2}  46.5468{col 37}{space 1}   21.52{col 46}{space 3}0.000{col 54}{space 4} 910.4943{col 67}{space 3} 1092.954
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(zloyalmedian=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:zloyalmedian}{space 4}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.00{col 38}{space 2}   0.9747
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 2.155578{col 26}{space 2} 67.99177{col 37}{space 5}-131.1058{col 51}{space 3}  135.417
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelD4bzloyal = r(table)
{txt}
{com}. mat list modelD4bzloyal
{res}
{txt}modelD4bzloyal[9,1]
             r2vs1.
               _at
     b {res}  2.1555782
{txt}    se {res}   67.99177
{txt}     z {res}  .03170352
{txt}pvalue {res}  .97470849
{txt}    ll {res} -131.10584
{txt}    ul {res}    135.417
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. 
. 
. 
. *Figure D1
. 
. matrix pointmodel = modelD1zloyal[1,1], modelD2zloyal[1,1], modelD3zloyal[1,1], modelD4zloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel = (modelD1zloyal[5,1], modelD2zloyal[5,1], modelD3zloyal[5,1], modelD4zloyal[5,1] \ modelD1zloyal[6,1], modelD2zloyal[6,1], modelD3zloyal[6,1], modelD4zloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model D1"  2 "Model D2"  3 "Model D3" 4 "Model D4", labsize(small) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE D1", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "[Unconditional Additive Effect]", size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD1.gph saved)

{com}. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *Figure 2
. 
. matrix pointmodel1 = modelD3azloyal[1,1], modelD3bzloyal[1,1], modelD4azloyal[1,1], modelD4bzloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel1 = (modelD3azloyal[5,1], modelD3bzloyal[5,1], modelD4azloyal[5,1], modelD4bzloyal[5,1] \ modelD3azloyal[6,1], modelD3bzloyal[6,1], modelD4azloyal[6,1], modelD4bzloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel1), ci((cimodel1))), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model D3" "Interquartile Change" "' 2 `" "Model D3" "Interdecile Change" "' 3 `" "Model D4" "Interquartile Change" "' 4 `" "Model D4" "Interdecile Change" "', labsize(small) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(-200(100)200, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE D2", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Unconditional Additive Effect]", size(small)) xline(0, lcolor(red%40) lpattern(dash))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureD2.gph saved)

{com}. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX D.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 09:53:59
{txt}{.-}
{smcl}
{txt}{sf}{ul off}